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DAMA-DMBOK
Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals. DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on ...
DAMA-DMBOK
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DAMA-DMBOK
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Data Modeling for MongoDB
Congratulations! You completed the MongoDB application within the given tight timeframe and there is a party to celebrate your application’s release into production. Although people are congratulating you at the celebration, you are feeling some uneasiness inside. To complete the project on time required making a lot of assumptions about the data, such as what terms meant and how calculations are derived. In addition, the poor documentation about the application will be of limited use to the support team, and not investigating all of the inherent rules in the data may eventually lead to poorly-performing structures in the not-so-distant future. Now, what if you had a time machine and could...
Data Modeling Made Simple with CA ERwin Data Modeler r8
Data Modeling Made Simple with CA ERwin Data Modeler r8 will provide the business or IT professional with a practical working knowledge of data modeling concepts and best practices, and how to apply these principles with CA ERwin Data Modeler r8. You’ll build many CA ERwin data models along the way, mastering first the fundamentals and later in the book the more advanced features of CA ERwin Data Modeler. This book combines real-world experience and best practices with down to earth advice, humor, and even cartoons to help you master the following ten objectives: 1. Understand the basics of data modeling and relational theory, and how to apply these skills using CA ERwin Data Modeler 2. Re...
Julia for Machine Learning
Unleash the power of Julia for your machine learning tasks. We reveal why Julia is chosen for more and more data science and machine learning projects, including Julia's ability to run algorithms at lightning speed. Next, we show you how to set up Julia and various IDEs such as Jupyter. Afterward, we explore key Julia libraries, which are useful for data science work, including packages related to visuals, data structures, and mathematical processes. After building a foundation in Julia, we dive into machine learning, with foundational concepts reinforced by Julia use cases. The use cases build upon each other, reaching the level where we code a machine learning model from scratch using Juli...
Building the Data Lakehouse
The data lakehouse is the next generation of the data warehouse and data lake, designed to meet today's complex and ever-changing analytics, machine learning, and data science requirements. Learn about the features and architecture of the data lakehouse, along with its powerful analytical infrastructure. Appreciate how the universal common connector blends structured, textual, analog, and IoT data. Maintain the lakehouse for future generations through Data Lakehouse Housekeeping and Data Future-proofing. Know how to incorporate the lakehouse into an existing data governance strategy. Incorporate data catalogs, data lineage tools, and open source software into your architecture to ensure your data scientists, analysts, and end users live happily ever after.
Fifty Years of Relational, and Other Database Writings
Fifty years of relational. It’s hard to believe the relational model has been around now for over half a century! But it has—it was born on August 19th, 1969, when Codd’s first database paper was published. And Chris Date has been involved with it for almost the whole of that time, working closely with Codd for many years and publishing the very first, and definitive, book on the subject in 1975. In this book’s title essay, Chris offers his own unique perspective (two chapters) on those fifty years. No database professional can afford to miss this one of a kind history. But there’s more to this book than just a little personal history. Another unique feature is an extensive and in ...
